Signaling, Random Assignment, and Causal Effect Estimation

34 Pages Posted: 12 Sep 2020

See all articles by Gilles Chemla

Gilles Chemla

Imperial College Business School; CNRS ; Centre for Economic Policy Research (CEPR)

Chris Hennessy

London Business School

Date Written: August 2020

Abstract

Causal evidence from random assignment has been labeled "the most credible." We argue it is generally incomplete in finance/economics, omitting central parts of the true empirical causal chain. Random assignment, in eliminating self-selection, simultaneously precludes signaling via treatment choice. However, outside experiments, agents enjoy discretion to signal, thereby causing changes in beliefs and outcomes. Therefore, if the goal is informing discretionary decisions, rather than predicting outcomes after forced/mistaken actions, randomization is problematic. As shown, signaling can amplify, attenuate, or reverse signs of causal effects. Thus, traditional methods of empirical finance, e.g. event studies, are often more credible/useful.

Keywords: Causal effect, CEO, Corporate Finance, Government Policy, household finance, investment, random assignment, selection, signal

JEL Classification: D82, E6, G14, G18, G28, G3, J24

Suggested Citation

Chemla, Gilles and Hennessy, Christopher, Signaling, Random Assignment, and Causal Effect Estimation (August 2020). CEPR Discussion Paper No. DP15175, Available at SSRN: https://ssrn.com/abstract=3688123

Gilles Chemla (Contact Author)

Imperial College Business School ( email )

South Kensington Campus
London SW7 2AZ, SW7 2AZ
United Kingdom
+44 207 594 9161 (Phone)
+44 207 594 9210 (Fax)

CNRS ( email )

Dauphine Recherches en Management
Place du Marechal de Lattre de Tassigny
Paris, 75016
France
331 44054970 (Phone)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Christopher Hennessy

London Business School ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

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